This Hidden Flaw Can Lead to Grave Errors in Economic Predictions

Daniel Kahneman, Princeton University emeritus professor of psychology and economics, and Nobel Prize winning author of “Thinking Fast, and Slow,” now warns that “noise” — variability in judgments that ought to be identical — causes costly errors.

In “Noise: A Flaw in Human Judgment” (Little, Brown Spark-May 16), Kahneman and his co-authors, Olivier Sibony and Cass R. Sunstein, explore just how harmful noise can be in numerous fields, including economic forecasting, and provide remedies to reduce it. 

“Unlike bias, noise isn’t intuitive, which is why we think we’ve discovered a new continent,” Sunstein says of the book’s new insights and ideas, in an interview with ThinkAdvisor.

A law professor on leave from Harvard University, Sunstein co-authored, with behavioral economist Richard Thaler, the bestseller “Nudge: Improving Decisions about Health, Wealth and Happiness.” Sibony is a professor of strategy at HEC Paris and an associate fellow at Oxford.

Financial advisors are noisy, doctors are noisy, judges are noisy — no one is immune, says Sunstein, who notes that “Noise” was more than five years in the writing. That includes Zoom meetings with Kahneman, who was awarded a Nobel in economic sciences in 2002 and the Presidential Medal of Freedom in 2013.

In the interview, Sunstein discusses ways that financial advisors can be less noisy — significant since decisions of all kinds involve predictive judgment, in which noise causes errors.

He also reveals the name of the prominent professor who helped him decide between two investment advisors to manage his and his wife’s stock and bond portfolio.

Sunstein is married to Samantha Power, who was recently confirmed to head the U.S. Agency for International Development and was a former Obama administration U.S. ambassador to the U.N.

In February, Sunstein was appointed senior counselor and regulatory policy officer in the Department of Homeland Security. He was head of the Office of Information and Regulatory Affairs during the Obama presidency.

In our interview, he stresses the need to reduce noise, which, the authors point out, can cause “grave damage.” One method is to think probabilistically — “a good thing to find in an advisor,” he remarks.

Sunstein, a former consultant to Apple years ago, also points to the remedy of “decision hygiene,” which may also help advisors deal with an expected increase in digital financial advice.

Even mood affects generating noise or being susceptible to it, Sunstein maintains in a short foray into what in psychology circles is known as “bull—- receptivity.”

ThinkAdvisor recently interviewed Sunstein, who was speaking by phone from Washington, D.C.  This April he released “Averting Catastrophe: Decision Theory for COVID-19, Climate Change and Potential Disasters of All Kinds” (NYU Press).

“There’s some overlap with ‘Noise’ because if you’re trying to reduce risk, you want to have some decision hygiene so your decisions aren’t noisy or biased,” he notes.

Here are highlights of our interview:

THINKADVISOR: Why is it important to be aware of noise? You write that it’s “rarely mentioned” and “goes unnoticed”; yet “it can cause grave damage.”

CASS SUNSTEIN: Noise is unwanted variability. Each of us is noisy, some more than others. We’re learning that noise is often the most important “character” in human life. Unlike bias, noise isn’t intuitive, which is why we think we discovered a new continent. Noise is like the character in the background of a movie that you don’t pay a lot of attention to but who turns out to be the most important one, as you learn at the end.

“Disagreement among traders makes markets,” you write. Is that an example of noise?

In markets, some people think a certain stock is going to go up; others think it’s not — and that’s part of the [process]. In terms of noise, we’re concerned with unwanted variability [judgments that should be identical], which isn’t good. 

“We’re really focused on reducing biases. Let’s also worry about reducing noise,” you three write. What’s the difference been bias and noise, in addition to what you’ve just mentioned?

With bias, there’s a tendency to go in one direction or another that’s really predictable. But when you enter a noisy system, you’re subject to a lottery. Bias overvalues the present and undervalues the future. If a person is too optimistic about how the economy will go, that’s a bias. 

Please elaborate.

If you’re at a firm where some people discriminate against men and some discriminate against women, those different biases will result in noise, where “different” people are going to be treated differently. 

Where else does noise occur?

In a hospital, if some of the doctors say, “Let’s wait and see if it gets better” and others say, “You need surgery.” Or if a doctor concludes in the morning that a person has a serious illness and probably should undergo extensive testing but in the afternoon, if he’s tired, decides to hold off on testing for six months, that’s a noisy doctor. In a system where the judges are very variable and one says, “five years in jail,” and the other says, “probation,” that’s a noisy legal system. 

Does this type of noise apply to inanimate objects, too?

Yes. A scale that’s sometimes five pounds too high and at other times five pounds too low is noisy. If you believe in that scale, you’re going to be extremely confused about your weight. A noisy scale can screw up your consumption pattern pretty badly even if the average is accurate.

Your book focuses on forecasting quite a bit. Psychologist Philip Tetlock’s research suggests that “detailed long-term predictions about specific events are simply impossible” but that “superforecasters” can predict short-term events of less than a year, you write. Please discuss the superior talent they have for thinking analytically and probabilistically.

Superforecasters are less noisy — they don’t show the variability that the rest of us show.  They’re very smart; but also, very importantly, they don’t think in terms of “yes” or “no” but in terms of probability. They break problems down to their component parts and don’t think holistically.